A probabilistic framework for memory-based reasoning

Artificial Intelligence

Volumn 104, Issue 1-2, 1998, Pages 287-311

  Kasif, Simon ^a   Salzberg, Steven ^b   Waltz, David ^c   Rachlin, John ^d   Aha, David W ^e  

^a University of Illinois   (United States)

^b Whiting School of Engineering   (United States)

^c NEC LABORATORIES AMERICA   (United States)

^d IBM T J WATSON RESEARCH CENTER   (United States)

^e NAVAL RESEARCH LABORATORY   (United States)

Author keywords

Bayes networks; Learning; Local learning; MBR transform; Memory based learning; Meta learning; Probabilistic inference

Indexed keywords

COMPUTER HARDWARE DESCRIPTION LANGUAGES; DATA STORAGE EQUIPMENT; INFERENCE ENGINES; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBABILITY;

BAYES NETWORKS; MEMORY-BASED REASONING (MBR);

ARTIFICIAL INTELLIGENCE;

EID: 0032156226     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0004-3702(98)00046-0     Document Type: Article

References (49)

1. D.W. Aha (Ed.), Special Issue on Lazy Learning (edited collection), Artificial Intelligence Review 11 (1-5) (1997).
2. D.W. Aha, D. Kibler, M. Albert, Instance-based learning algorithms, Machine Learning 6 (1) (1991).
3. C. Atkeson, A. Moore, S. Schaal, Locally weighted learning, Artificial Intelligence Review 11 (1997) 11-73.
4. C. Atkeson, A. Moore, S. Schaal, Memory-based learning for control, Artificial Intelligence Review 11 (1997) 75-113.
5. M. Aoki, On some convergence questions in Bayesian optimization problems, IEEE Transactions on Automatic Control 10 (1965) 180-182.
6. R. Albrecht, W. Werner, Error analysis of a statistical decision method, IEEE Transactions on Information Theory 10 (1968) 34-38.
7. J.L. Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM 18 (9) (1975) 509-517.
8. J. Breese, D. Heckerman, Decision-theoretic case-based reasoning, IEEE Transactions on Systems, Man and Cybernetics 26 (6) (1998) 838-842.
9. L. Bottou, V. Vapnik, Local learning algorithms, Neural Computation 4 (1992) 888-900.
10. B. Chandrasekan, T. Harley, Comments on the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory 15 (1969) 421-423.
11. B. Chandrasekan, Independence of measurements and the mean recognition accuracy, IEEE Transactions on Information Theory 17 (1971) 452-456.
12. R. Creecy, B. Masand, S. Smith, D.L. Waltz, Trading MIPS and memory for knowledge engineering, Communications of the ACM 35 (8) (1992) 48-64.
13. P.E. Clark, T. Niblett, The CN2 induction algorithm, Machine Learning 3 (1989) 261-284.
14. S. Cost, S. Salzberg, A weighted nearest neighbor algorithm for learning with symbolic features, Machine Learning 10 (1) (1993) 57-78.
15. Belur V. Dasarathy (Ed.), Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, IEEE Computer Society Press, Los Alamitos, CA, 1991.
16. R. Duda, P. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
17. S. Edelman, Representation, Similarity and the Chorus of Prototypes, Minds and Machines, 1994.
18. J.H. Friedman, Flexible metric nearest neighbor classification, Technical Report, Stanford University, Statistics Dept., November 1994.
19. Z. Gilboa, D. Schmeidler, Case-based decision theory, Technical Report, Northwestern University, Evanston, IL, 1994.
20. R. Holte, Very simple classification rules perform well on most commonly used datasets, Machine Learning 11 (1) (1993) 63-90.
21. T. Hastie, R. Tibshirani, Discriminant adaptive nearest neighbor classification, Technical Report, Stanford University, Statistics Dept., December 1994.
22. G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory 14 (1968) 55-63.
23. D. Jones, W. Taylor, J. Thornton, The rapid generation of mutation data matrices from protein sequences, CABIOS 8 (3) (1992) 275-282.
24. J.B. Kruskal, M. Wish, Multidimensional Scaling, Sage Publications, 1978.
25. S.L. Lauritzen, The em algorithm for graphical association models with missing data, Computational Statistics and Data Analysis (1995) 191-201.
26. P. Langley, W. Iba, An analysis of Bayesian classifiers, in: Proc. 10th National Conf. on Artificial Intelligence (AAAI-92), San Jose, CA, AAAI Press, 1992, pp. 223-228.
27. P. Maes, R. Kozierok, Learning interface agents, in: Proc. 11th National Conf. on Artificial Intelligence (AAAI-93), Washington, DC, MIT Press, Cambridge, MA, 1993, pp. 459-465.
28. B. Masand, G. Linoff, D.L. Waltz, Classifying news stories using memory based reasoning, in: Proc. SIGIR, 1992, pp. 59-65.
29. P.M. Murphy, UCI repository of machine learning databases - a machine-readable data repository, Maintained at the Department of Information and Computer Science, University of California, Irvine, CA; Anonymous FTP from ics.uci.edu in the directory pub/machine-learning-databases, 1995.
30. E.A. Nadaraya, On estimating regression, Theory of Probability and Its Applications 9 (1964) 141-142.
31. S. Omohundro, Efficient algorithms with neural network behavior, Complex Systems, 1987, 273-347.
32. S. Omohundro, Five balltree construction algorithms, Technical Report 89-063, International Computer Science Institute, Berkeley, CA, 1989.
33. J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, Los Altos, CA, 1988.
34. M. Pazzani, W. Sarrett, Average case analysis of conjunctive learning algorithms, in: Proc. Workshop on Machine Learning, Austin, TX, 1990, pp. 339-347.
35. F. Pereira, N. Tishby, L. Lee, Distributional clustering of English words, in: Proc. 30th Meeting of the Association for Computational Linguistics, 1993, pp. 183-190.
36. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.
37. J. Rachlin, S. Kasif, S. Salzberg, D.W. Aha, Towards a better understanding of memory-based and Bayesian classifiers, in: Proc. 11th International Conf. on Machine Learning, New Brunswick, NJ, July 1994, pp. 242-250.
38. R.M. Royall, A class of nonparametric estimators of a smooth regression function, Ph.D. thesis, Department of Statistics, Stanford University, 1966.
39. S. Salzberg, S. Cost, Predicting protein secondary structure with a nearest-neighbor algorithm, Journal of Molecular Biology 227 (1992) 371-374.
40. D.J. Spiegelhalter, A.P. Dawid, S.L. Lauritzen, R.G. Cowell, Bayesian analysis in expert systems, Statistical Science 8 (3) (1993) 219-283.
41. R.N. Sheppard, Multidimensional scaling, tree fitting and clustering, Science (1980) 1317-1323.
42. C. Stanfill, D. Waltz, Toward memory-based reasoning, Communications of the ACM 29 (12) (1986) 1213-1228.
43. H. Turtle, W. Bruce Croft, Evaluation of an inference network-based retrieval model, ACM Transactions on Information Systems (1991).
44. D.M. Titterington, A. Smith, U.E. Makov, Statistical Analysis of Finite Mixture Distributions, Wiley, New York, 1985.
45. A. Tversky, Features of similarity, Psychological Review 84 (1977) 324-357.
46. L. Valiant, A theory of the learnable, Communications of the ACM 27 (1984) 1134-1142.
47. V. Vapnik, L. Bottou, Local learning algorithms for pattern recognition and dependency estimation, Neural Computation 5 (1993) 893-909.
48. Xiru Zhang, J.P. Mesirov, D.L. Waltz, A hybrid system for protein secondary structure prediction, Journal of Molecular Biology 225 (1992) 1049-1063.
49. T.-M. Yi, E. Lander, Protein secondary structure prediction using nearest-neighbor methods, Journal of Molecular Biology 232 (1993) 1117-1129.